Explaining the AI black box problem

ZDNET
27 Apr 202007:01

TLDRIn this discussion, Tanya Hall interviews Sheldon Fernandez, CEO of Darwin AI, about the AI black box problem. Fernandez explains that while AI is powerful, its decision-making processes are often opaque. Darwin AI's technology aims to make these processes transparent, allowing for trust in AI systems. The conversation covers real-world examples, such as autonomous vehicles making decisions based on incorrect correlations, and the importance of understanding AI methodologies. Darwin AI's research introduces a framework for validating AI explanations, emphasizing the need for robust AI systems that can be trusted by both developers and end-users.

Takeaways

  • 🧠 The AI black box problem refers to the lack of transparency in how neural networks reach their conclusions, despite their effectiveness.
  • 🤖 Darwin AI is known for addressing the black box issue in AI, aiming to make neural network processes more understandable.
  • 🔍 Deep learning, a subset of machine learning, involves training neural networks with vast amounts of data, but the internal workings remain a mystery.
  • 🦁 An example given is training a neural network to recognize lions by showing it millions of pictures, yet the reasoning behind its recognition is unclear.
  • 👀 The black box can lead to incorrect conclusions based on unrecognized patterns, such as a network identifying horses by copyright symbols rather than the horse itself.
  • 🚗 A real-world scenario involves an autonomous vehicle making turns based on the color of the sky, due to a correlation picked up during training data.
  • 🤝 To understand neural networks, Darwin AI uses other forms of AI to analyze and explain the complex variables and layers within neural networks.
  • 📈 Darwin AI's research introduces a counterfactual approach to validate explanations, by testing if decisions change when hypothetical reasons are removed.
  • 📚 The importance of building a foundational understanding of explainability among technical professionals to ensure robust AI systems.
  • 🗣️ Explainability to end-users, such as patients understanding an AI's medical diagnosis, builds on the technical understanding and is crucial for trust.
  • 🔗 Darwin AI encourages industry professionals to connect through their website, LinkedIn, or email for further discussions on AI transparency and solutions.

Q & A

  • What is the AI black box problem?

    -The AI black box problem refers to the lack of transparency in how artificial intelligence systems, particularly neural networks, make decisions. These systems can perform tasks very well but do not provide insight into the reasoning behind their conclusions, making it difficult to understand and trust their outputs.

  • What is Darwin AI known for?

    -Darwin AI is known for addressing the black box problem in artificial intelligence. They have developed technology that aims to make AI's decision-making process more understandable and transparent.

  • How does a neural network learn to recognize objects?

    -A neural network learns to recognize objects by being shown thousands or even hundreds of thousands of examples of data. Through this process, it gradually trains itself to become proficient at identifying specific objects, such as a lion in the provided example.

  • Why is it a problem if an AI system reaches the correct conclusion for the wrong reasons?

    -If an AI system reaches the correct conclusion for the wrong reasons, it can lead to unreliable and potentially harmful outcomes. It is important to understand the rationale behind AI decisions to ensure they are based on valid and ethical considerations.

  • Can you provide an example of the black box problem in real-world scenarios?

    -One example given in the script is an autonomous vehicle that turned left more frequently when the sky was a certain shade of purple. It turned out the AI had correlated the color of the sky with the turning direction based on training data from the Nevada desert, which is a nonsensical correlation.

  • How does Darwin AI's technology help in understanding neural networks?

    -Darwin AI uses other forms of artificial intelligence to analyze and understand the complex workings of neural networks. This technology surfaces explanations for the decisions made by neural networks, helping to demystify the black box.

  • What is the counterfactual approach mentioned in the script?

    -The counterfactual approach is a method used to validate the explanations generated by AI. It involves removing the hypothetical reasons thought to be influencing a decision from the input and observing if the decision changes significantly, thus confirming the validity of the explanation.

  • How does Darwin AI ensure the validity of the explanations generated by their AI technology?

    -Darwin AI uses a framework that includes the counterfactual approach to test the validity of explanations. By altering the inputs and observing changes in the AI's decisions, they can confirm whether the generated explanations are accurate.

  • What are the different levels of explainability mentioned in the script?

    -The script mentions two levels of explainability: one for the technical audience, such as engineers and data scientists, which helps them understand and improve the AI system; and another for the end-user, such as explaining to a radiologist why a certain diagnosis was made.

  • What recommendations does Sheldon Fernandez have for those contemplating an AI solution or who already have one in place?

    -Sheldon Fernandez recommends focusing on building foundational explainability for technical professionals to ensure robustness and confidence in their AI models. Once that is established, they can then work on explaining the AI's decisions to end-users in a way that is understandable and trustworthy.

  • How can someone connect with Sheldon Fernandez or learn more about Darwin AI?

    -To connect with Sheldon Fernandez or learn more about Darwin AI, one can visit their website at darwina.ai, find Sheldon on LinkedIn, or email him directly at [email protected].

Outlines

00:00

🧠 Solving the AI Black Box Problem

In this segment, Tanya Hall interviews Sheldon Fernandez, CEO of Darwin AI, about the company's efforts to address the 'black box' issue in artificial intelligence. Darwin AI is recognized for its work on making AI's decision-making processes more transparent. The conversation delves into the complexities of neural networks and deep learning, which are powerful but often operate without clear insight into how they reach their conclusions. An example of an AI misidentifying horses due to a copyright symbol is given to illustrate the problem. The segment also discusses the practical implications of the black box issue, such as an autonomous vehicle making turns based on an irrelevant correlation with the color of the sky.

05:02

🔍 Enhancing Trust in AI Decisions

This paragraph focuses on Darwin AI's approach to understanding and explaining the methodology behind neural networks. It highlights the use of artificial intelligence to interpret other AI systems, emphasizing the mathematical infeasibility of manually analyzing complex neural networks. The company's intellectual property, developed by Canadian academics, surfaces explanations for AI decisions. The segment also discusses Darwin AI's research published in December, which introduced a counterfactual framework to validate AI-generated explanations. This framework involves altering inputs to see if the AI's decision changes significantly, thereby confirming the validity of the explanation. The conversation concludes with advice for those implementing AI solutions, emphasizing the importance of building a robust foundational understanding of AI explainability among technical professionals before extending it to end-users.

Mindmap

Keywords

💡AI black box problem

The AI black box problem refers to the lack of transparency in how artificial intelligence systems, particularly neural networks, arrive at their decisions. It's a significant issue because while AI can perform complex tasks, we often don't understand the internal mechanisms that lead to its outputs. In the video, this problem is highlighted as a major challenge in the industry, affecting trust and reliability in AI systems.

💡Darwin AI

Darwin AI is the company mentioned in the script, known for addressing the black box problem in AI. The company's technology aims to make AI's decision-making process more understandable and transparent. The script discusses how Darwin AI's research and technology can help businesses and industries trust AI-generated explanations.

💡Neural networks

Neural networks are a subset of deep learning, which is a part of machine learning and, by extension, artificial intelligence. They function by analyzing vast amounts of data to learn tasks such as image recognition. The script uses the example of training a neural network to recognize lions by showing it numerous pictures of lions. However, the internal workings of these networks are often opaque, contributing to the black box problem.

💡Deep learning

Deep learning is a branch of machine learning that involves training artificial neural networks with many layers to learn complex patterns in data. It's instrumental in achieving high performance in tasks like image and speech recognition. The script mentions deep learning as a powerful yet enigmatic facet of AI that is part of the black box issue.

💡Explainability

Explainability in AI refers to the ability to understand and interpret the decision-making process of an AI system. It's crucial for building trust and ensuring that AI systems are used responsibly. The script discusses the importance of explainability for both developers and end-users, emphasizing the need for clear explanations of AI decisions.

💡Counterfactual approach

The counterfactual approach is a method used to test the validity of AI-generated explanations. It involves altering the input data to see if the AI's decision changes significantly, thus confirming or refuting the proposed explanation. The script describes this approach as a framework developed by Darwin AI to ensure the reliability of their explanations.

💡Autonomous vehicles

Autonomous vehicles, or self-driving cars, are a real-world application of AI where the black box problem can have serious implications. The script provides an example where an autonomous vehicle turned left based on an irrelevant factor—the color of the sky—due to a spurious correlation learned during training, highlighting the potential dangers of not understanding AI decision-making.

💡Non-sensible correlation

A non-sensible correlation is an association that an AI system might incorrectly infer from the data, leading to illogical or incorrect decisions. In the script, it is exemplified by the autonomous vehicle scenario where the AI correlated the color of the sky with the direction to turn, which is not a sensible basis for decision-making.

💡Technical understanding

Technical understanding is the comprehension of the underlying mechanisms and processes of AI systems by engineers and data scientists. The script suggests that having a solid technical understanding is foundational for building robust AI models and is the first step towards achieving explainability for end-users.

💡Robustness

Robustness in the context of AI refers to the ability of a system to perform well and make accurate decisions even in edge cases or when faced with unusual or unexpected inputs. The script mentions that understanding explainability helps in creating AI systems that are more robust and can handle a wider range of scenarios.

💡Consumer explainability

Consumer explainability is the level of understanding provided to the end-user of an AI system, explaining why a particular decision was made. For instance, the script mentions explaining to a radiologist why an AI classified an image as indicative of cancer. This level of transparency is essential for user trust and acceptance of AI decisions.

Highlights

Tanya Hall and Sheldon Fernandez discuss the AI black box problem.

Darwin AI is known for cracking the black box problem in AI.

Artificial intelligence operates as a black box, performing tasks without revealing how.

Neural networks learn from vast amounts of data but lack transparency in their decision-making process.

An example of a neural network incorrectly identifying horses based on copyright symbols.

The black box problem leads to correct answers for wrong reasons.

A real-world scenario of an autonomous vehicle making turns based on the color of the sky.

Darwin AI's technology helped identify the non-sensible correlation in the autonomous vehicle's AI.

Understanding neural networks is mathematically infeasible due to their complexity.

Darwin AI uses other forms of AI to understand and explain neural networks.

A framework for validating AI explanations through counterfactual approaches.

Darwin AI's research shows their technique outperforms state-of-the-art methods.

Importance of building foundational explainability for engineers and data scientists.

The necessity of technical understanding before explaining AI decisions to consumers.

Sheldon Fernandez's recommendations on explaining AI and ensuring trust in results.

How to connect with Sheldon Fernandez and Darwin AI for further inquiries.

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